Overview

Dataset statistics

Number of variables17
Number of observations11430
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory122.0 B

Variable types

Text2
Categorical4
Numeric8
Unsupported1
Boolean2

Alerts

Has_Url_Pattern has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Entropy is highly overall correlated with Has_File_Path and 1 other fieldsHigh correlation
Has_File_Path is highly overall correlated with EntropyHigh correlation
Has_Uncommon_Chars is highly overall correlated with Special_Char_CountHigh correlation
Hierarchy_Level is highly overall correlated with Slash_CountHigh correlation
Parameter_Count is highly overall correlated with EntropyHigh correlation
Slash_Count is highly overall correlated with Hierarchy_LevelHigh correlation
Special_Char_Count is highly overall correlated with Has_Uncommon_CharsHigh correlation
Has_File_Path is highly imbalanced (68.9%)Imbalance
Entropy is highly imbalanced (55.7%)Imbalance
status_numerico is uniformly distributedUniform
Dot_Positions is an unsupported type, check if it needs cleaning or further analysisUnsupported
Number_Count has 6566 (57.4%) zerosZeros
Special_Char_Count has 7510 (65.7%) zerosZeros

Reproduction

Analysis started2024-02-10 00:26:47.433885
Analysis finished2024-02-10 00:26:51.234167
Duration3.8 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

url
Text

Distinct11429
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:51.679561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length1641
Median length439
Mean length61.120035
Min length12

Characters and Unicode

Total characters698602
Distinct characters100
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11428 ?
Unique (%)> 99.9%

Sample

1st rowhttp://www.crestonwood.com/router.php
2nd rowhttp://shadetreetechnology.com/V4/validation/a111aedc8ae390eabcfa130e041a10a4
3rd rowhttps://support-appleld.com.secureupdate.duilawyeryork.com/ap/89e6a3b4b063b8d/?cmd=_update&dispatch=89e6a3b4b063b8d1b&locale=_
4th rowhttp://rgipt.ac.in
5th rowhttp://www.iracing.com/tracks/gateway-motorsports-park/
ValueCountFrequency (%)
http://stolizaparketa.ru/wp-content/themes/twentyfifteen/css/read/chinavali/index.php?email=_xxx@yyy.com 7
 
0.1%
http://153284594738391.statictab.com/2506080 3
 
< 0.1%
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo 2
 
< 0.1%
http://www.paypal-verification.applmanager.com/customer_center/user-478741 2
 
< 0.1%
http://tokokainbandung.com/wp-content/themes/theretailer/inc/addons/login/customer_center/customer-idpp00c672/myaccount/signin 2
 
< 0.1%
https://sites.google.com/site/recoveryfbconfirmcontactus 2
 
< 0.1%
https://milenyumpark.com.tr/iletisim 2
 
< 0.1%
http://www.courgeon-immobilier.fr/wp-content/uploads/2019/07/tpg/fa2acd5487b1bef895a7453e5dc96013 2
 
< 0.1%
https://www.zabor-vn.com/system/csvprice_pro/smart/customer_center/customer-idpp00c354/myaccount/signin 2
 
< 0.1%
https://elhagearms.com/jppp/toda 2
 
< 0.1%
Other values (11244) 11407
99.8%
2024-02-09T18:26:52.211863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 49171
 
7.0%
/ 49030
 
7.0%
e 40829
 
5.8%
o 35303
 
5.1%
a 32792
 
4.7%
p 30923
 
4.4%
s 29208
 
4.2%
c 28485
 
4.1%
. 28354
 
4.1%
i 27617
 
4.0%
Other values (90) 346890
49.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 492123
70.4%
Other Punctuation 95110
 
13.6%
Decimal Number 62318
 
8.9%
Uppercase Letter 30171
 
4.3%
Dash Punctuation 11402
 
1.6%
Connector Punctuation 3688
 
0.5%
Math Symbol 3552
 
0.5%
Control 65
 
< 0.1%
Open Punctuation 64
 
< 0.1%
Close Punctuation 63
 
< 0.1%
Other values (4) 46
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1951
 
6.5%
D 1674
 
5.5%
S 1508
 
5.0%
C 1502
 
5.0%
F 1499
 
5.0%
E 1399
 
4.6%
B 1368
 
4.5%
N 1287
 
4.3%
T 1274
 
4.2%
M 1266
 
4.2%
Other values (18) 15443
51.2%
Lowercase Letter
ValueCountFrequency (%)
t 49171
 
10.0%
e 40829
 
8.3%
o 35303
 
7.2%
a 32792
 
6.7%
p 30923
 
6.3%
s 29208
 
5.9%
c 28485
 
5.8%
i 27617
 
5.6%
r 23269
 
4.7%
n 22460
 
4.6%
Other values (17) 172066
35.0%
Other Punctuation
ValueCountFrequency (%)
/ 49030
51.6%
. 28354
29.8%
: 11749
 
12.4%
& 1855
 
2.0%
? 1614
 
1.7%
% 1407
 
1.5%
; 712
 
0.7%
@ 254
 
0.3%
# 50
 
0.1%
, 46
 
< 0.1%
Other values (3) 39
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 8092
13.0%
0 7889
12.7%
1 7375
11.8%
3 6154
9.9%
4 5713
9.2%
7 5622
9.0%
5 5578
9.0%
6 5386
8.6%
8 5319
8.5%
9 5190
8.3%
Math Symbol
ValueCountFrequency (%)
= 3351
94.3%
+ 120
 
3.4%
~ 78
 
2.2%
< 3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 45
70.3%
[ 10
 
15.6%
{ 9
 
14.1%
Close Punctuation
ValueCountFrequency (%)
) 45
71.4%
] 10
 
15.9%
} 8
 
12.7%
Control
ValueCountFrequency (%)
‚ 33
50.8%
ƒ 31
47.7%
‘ 1
 
1.5%
Modifier Symbol
ValueCountFrequency (%)
` 17
89.5%
^ 2
 
10.5%
Space Separator
ValueCountFrequency (%)
  2
66.7%
1
33.3%
Other Letter
ValueCountFrequency (%)
æ‹  1
50.0%
å‚… 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 11402
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3688
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 522293
74.8%
Common 176307
 
25.2%
Han 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 49171
 
9.4%
e 40829
 
7.8%
o 35303
 
6.8%
a 32792
 
6.3%
p 30923
 
5.9%
s 29208
 
5.6%
c 28485
 
5.5%
i 27617
 
5.3%
r 23269
 
4.5%
n 22460
 
4.3%
Other values (44) 202236
38.7%
Common
ValueCountFrequency (%)
/ 49030
27.8%
. 28354
16.1%
: 11749
 
6.7%
- 11402
 
6.5%
2 8092
 
4.6%
0 7889
 
4.5%
1 7375
 
4.2%
3 6154
 
3.5%
4 5713
 
3.2%
7 5622
 
3.2%
Other values (34) 34927
19.8%
Han
ValueCountFrequency (%)
æ‹  1
50.0%
å‚… 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 698466
> 99.9%
None 134
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 49171
 
7.0%
/ 49030
 
7.0%
e 40829
 
5.8%
o 35303
 
5.1%
a 32792
 
4.7%
p 30923
 
4.4%
s 29208
 
4.2%
c 28485
 
4.1%
. 28354
 
4.1%
i 27617
 
4.0%
Other values (81) 346754
49.6%
None
ValueCountFrequency (%)
‚ 33
24.6%
 33
24.6%
à 33
24.6%
ƒ 31
23.1%
  2
 
1.5%
µ 1
 
0.7%
‘ 1
 
0.7%
CJK
ValueCountFrequency (%)
æ‹  1
50.0%
å‚… 1
50.0%

status_numerico
Categorical

UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
5715 
1
5715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Length

2024-02-09T18:26:52.268731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-09T18:26:52.308414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring characters

ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Subdomain_Count
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0524934
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.342502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum14
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86109888
Coefficient of variation (CV)0.41953795
Kurtosis26.648208
Mean2.0524934
Median Absolute Deviation (MAD)0
Skewness3.2559518
Sum23460
Variance0.74149129
MonotonicityNot monotonic
2024-02-09T18:26:52.383101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7803
68.3%
1 2058
 
18.0%
3 785
 
6.9%
4 640
 
5.6%
5 96
 
0.8%
6 15
 
0.1%
7 10
 
0.1%
12 9
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
1 2058
 
18.0%
2 7803
68.3%
3 785
 
6.9%
4 640
 
5.6%
5 96
 
0.8%
6 15
 
0.1%
7 10
 
0.1%
8 7
 
0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
< 0.1%
12 9
 
0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 7
 
0.1%
7 10
 
0.1%
6 15
 
0.1%
5 96
 
0.8%
4 640
5.6%

Domain_Length
Real number (ℝ)

Distinct83
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.090289
Minimum4
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.430072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q115
median19
Q324
95-th percentile42
Maximum214
Range210
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.777171
Coefficient of variation (CV)0.51100159
Kurtosis69.829931
Mean21.090289
Median Absolute Deviation (MAD)4
Skewness5.1600778
Sum241062
Variance116.14742
MonotonicityNot monotonic
2024-02-09T18:26:52.479793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 956
 
8.4%
15 754
 
6.6%
18 731
 
6.4%
17 725
 
6.3%
14 702
 
6.1%
19 586
 
5.1%
20 577
 
5.0%
21 561
 
4.9%
13 531
 
4.6%
22 490
 
4.3%
Other values (73) 4817
42.1%
ValueCountFrequency (%)
4 14
 
0.1%
5 16
 
0.1%
6 71
 
0.6%
7 71
 
0.6%
8 61
 
0.5%
9 102
 
0.9%
10 216
1.9%
11 320
2.8%
12 370
3.2%
13 531
4.6%
ValueCountFrequency (%)
214 2
< 0.1%
213 3
< 0.1%
212 1
 
< 0.1%
211 1
 
< 0.1%
179 1
 
< 0.1%
150 1
 
< 0.1%
122 1
 
< 0.1%
120 1
 
< 0.1%
95 1
 
< 0.1%
87 1
 
< 0.1%

Has_File_Path
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
10791 
0
 
639

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Length

2024-02-09T18:26:52.525061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-09T18:26:52.561051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10791
94.4%
0 639
 
5.6%

Parameter_Count
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1565179
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.594273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum20
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81411714
Coefficient of variation (CV)0.70393819
Kurtosis144.16276
Mean1.1565179
Median Absolute Deviation (MAD)0
Skewness9.9373666
Sum13219
Variance0.66278672
MonotonicityNot monotonic
2024-02-09T18:26:52.635267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 10705
93.7%
3 435
 
3.8%
2 157
 
1.4%
4 54
 
0.5%
10 16
 
0.1%
5 16
 
0.1%
7 12
 
0.1%
6 10
 
0.1%
11 7
 
0.1%
9 6
 
0.1%
Other values (5) 12
 
0.1%
ValueCountFrequency (%)
1 10705
93.7%
2 157
 
1.4%
3 435
 
3.8%
4 54
 
0.5%
5 16
 
0.1%
6 10
 
0.1%
7 12
 
0.1%
8 3
 
< 0.1%
9 6
 
0.1%
10 16
 
0.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
12 4
 
< 0.1%
11 7
0.1%
10 16
0.1%
9 6
 
0.1%
8 3
 
< 0.1%
7 12
0.1%
6 10
0.1%

Slash_Count
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2895888
Minimum2
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.675835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q35
95-th percentile8
Maximum33
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8822513
Coefficient of variation (CV)0.43879528
Kurtosis18.302403
Mean4.2895888
Median Absolute Deviation (MAD)1
Skewness2.7313119
Sum49030
Variance3.54287
MonotonicityNot monotonic
2024-02-09T18:26:52.716429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 4252
37.2%
4 2675
23.4%
5 1765
15.4%
6 881
 
7.7%
2 639
 
5.6%
7 548
 
4.8%
8 269
 
2.4%
9 183
 
1.6%
10 122
 
1.1%
11 34
 
0.3%
Other values (12) 62
 
0.5%
ValueCountFrequency (%)
2 639
 
5.6%
3 4252
37.2%
4 2675
23.4%
5 1765
15.4%
6 881
 
7.7%
7 548
 
4.8%
8 269
 
2.4%
9 183
 
1.6%
10 122
 
1.1%
11 34
 
0.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
23 3
< 0.1%
21 6
0.1%
20 2
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
14 6
0.1%

Dot_Positions
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size89.4 KiB

Number_Count
Real number (ℝ)

ZEROS 

Distinct130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4521435
Minimum0
Maximum679
Zeros6566
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.762784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile24
Maximum679
Range679
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.319904
Coefficient of variation (CV)2.9933005
Kurtosis315.7282
Mean5.4521435
Median Absolute Deviation (MAD)0
Skewness12.097102
Sum62318
Variance266.33925
MonotonicityNot monotonic
2024-02-09T18:26:52.814375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6566
57.4%
1 660
 
5.8%
2 483
 
4.2%
4 397
 
3.5%
3 379
 
3.3%
6 376
 
3.3%
5 284
 
2.5%
8 224
 
2.0%
7 187
 
1.6%
10 160
 
1.4%
Other values (120) 1714
 
15.0%
ValueCountFrequency (%)
0 6566
57.4%
1 660
 
5.8%
2 483
 
4.2%
3 379
 
3.3%
4 397
 
3.5%
5 284
 
2.5%
6 376
 
3.3%
7 187
 
1.6%
8 224
 
2.0%
9 137
 
1.2%
ValueCountFrequency (%)
679 1
< 0.1%
269 2
< 0.1%
267 1
< 0.1%
256 1
< 0.1%
233 1
< 0.1%
222 1
< 0.1%
220 1
< 0.1%
212 1
< 0.1%
211 1
< 0.1%
201 1
< 0.1%

Hierarchy_Level
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2487314
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:52.860871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7740475
Coefficient of variation (CV)0.54607391
Kurtosis11.540582
Mean3.2487314
Median Absolute Deviation (MAD)1
Skewness2.1770892
Sum37133
Variance3.1472444
MonotonicityNot monotonic
2024-02-09T18:26:52.902616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 4306
37.7%
3 2676
23.4%
4 1764
15.4%
5 878
 
7.7%
1 639
 
5.6%
6 539
 
4.7%
7 258
 
2.3%
8 188
 
1.6%
9 106
 
0.9%
10 32
 
0.3%
Other values (10) 44
 
0.4%
ValueCountFrequency (%)
1 639
 
5.6%
2 4306
37.7%
3 2676
23.4%
4 1764
15.4%
5 878
 
7.7%
6 539
 
4.7%
7 258
 
2.3%
8 188
 
1.6%
9 106
 
0.9%
10 32
 
0.3%
ValueCountFrequency (%)
28 1
 
< 0.1%
26 1
 
< 0.1%
22 2
 
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
13 3
 
< 0.1%
12 14
0.1%
11 17
0.1%
Distinct288
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:53.031335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length2.7834646
Min length0

Characters and Unicode

Total characters31815
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)1.1%

Sample

1st rowcom
2nd rowcom
3rd rowcom
4th rowin
5th rowcom
ValueCountFrequency (%)
com 6997
61.2%
org 645
 
5.6%
net 377
 
3.3%
ru 294
 
2.6%
uk 193
 
1.7%
de 134
 
1.2%
au 129
 
1.1%
fr 90
 
0.8%
br 89
 
0.8%
io 88
 
0.8%
Other values (277) 2392
 
20.9%
2024-02-09T18:26:53.236821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 8035
25.3%
c 7280
22.9%
m 7131
22.4%
r 1387
 
4.4%
u 900
 
2.8%
e 881
 
2.8%
n 788
 
2.5%
g 786
 
2.5%
t 674
 
2.1%
i 571
 
1.8%
Other values (27) 3382
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31570
99.2%
Decimal Number 243
 
0.8%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8035
25.5%
c 7280
23.1%
m 7131
22.6%
r 1387
 
4.4%
u 900
 
2.9%
e 881
 
2.8%
n 788
 
2.5%
g 786
 
2.5%
t 674
 
2.1%
i 571
 
1.8%
Other values (16) 3137
 
9.9%
Decimal Number
ValueCountFrequency (%)
1 49
20.2%
2 38
15.6%
5 28
11.5%
3 23
9.5%
7 21
8.6%
8 21
8.6%
6 20
8.2%
4 19
 
7.8%
9 12
 
4.9%
0 12
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31570
99.2%
Common 245
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8035
25.5%
c 7280
23.1%
m 7131
22.6%
r 1387
 
4.4%
u 900
 
2.9%
e 881
 
2.8%
n 788
 
2.5%
g 786
 
2.5%
t 674
 
2.1%
i 571
 
1.8%
Other values (16) 3137
 
9.9%
Common
ValueCountFrequency (%)
1 49
20.0%
2 38
15.5%
5 28
11.4%
3 23
9.4%
7 21
8.6%
8 21
8.6%
6 20
8.2%
4 19
 
7.8%
9 12
 
4.9%
0 12
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8035
25.3%
c 7280
22.9%
m 7131
22.4%
r 1387
 
4.4%
u 900
 
2.8%
e 881
 
2.8%
n 788
 
2.5%
g 786
 
2.5%
t 674
 
2.1%
i 571
 
1.8%
Other values (27) 3382
10.6%

Has_Uncommon_Chars
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
False
7510 
True
3920 
ValueCountFrequency (%)
False 7510
65.7%
True 3920
34.3%
2024-02-09T18:26:53.300202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
False
9686 
True
1744 
ValueCountFrequency (%)
False 9686
84.7%
True 1744
 
15.3%
2024-02-09T18:26:53.331310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Vowel_Consonant_Ratio
Real number (ℝ)

Distinct958
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41591002
Minimum0
Maximum1.1176471
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:53.374410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.33333333
median0.41666667
Q30.5
95-th percentile0.61904762
Maximum1.1176471
Range1.1176471
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.1277548
Coefficient of variation (CV)0.30716934
Kurtosis0.20838194
Mean0.41591002
Median Absolute Deviation (MAD)0.083333333
Skewness-0.050938555
Sum4753.8515
Variance0.01632129
MonotonicityNot monotonic
2024-02-09T18:26:53.521160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 509
 
4.5%
0.3333333333 451
 
3.9%
0.4 278
 
2.4%
0.4285714286 210
 
1.8%
0.25 178
 
1.6%
0.375 178
 
1.6%
0.4444444444 142
 
1.2%
0.2857142857 139
 
1.2%
0.3636363636 131
 
1.1%
0.3529411765 121
 
1.1%
Other values (948) 9093
79.6%
ValueCountFrequency (%)
0 24
0.2%
0.05555555556 1
 
< 0.1%
0.05882352941 2
 
< 0.1%
0.0625 5
 
< 0.1%
0.06666666667 14
0.1%
0.06976744186 1
 
< 0.1%
0.07142857143 9
 
0.1%
0.07692307692 13
0.1%
0.08333333333 20
0.2%
0.08695652174 2
 
< 0.1%
ValueCountFrequency (%)
1.117647059 1
< 0.1%
1 1
< 0.1%
0.9545454545 1
< 0.1%
0.9285714286 1
< 0.1%
0.9 1
< 0.1%
0.875 1
< 0.1%
0.8644067797 1
< 0.1%
0.8333333333 2
< 0.1%
0.8275862069 1
< 0.1%
0.8222222222 1
< 0.1%

Special_Char_Count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5682415
Minimum0
Maximum29
Zeros7510
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-09T18:26:53.572855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.7843047
Coefficient of variation (CV)2.413088
Kurtosis16.41615
Mean1.5682415
Median Absolute Deviation (MAD)0
Skewness3.7942926
Sum17925
Variance14.320962
MonotonicityNot monotonic
2024-02-09T18:26:53.621509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 7510
65.7%
1 1279
 
11.2%
2 620
 
5.4%
3 600
 
5.2%
4 248
 
2.2%
5 236
 
2.1%
6 157
 
1.4%
7 119
 
1.0%
8 82
 
0.7%
9 71
 
0.6%
Other values (20) 508
 
4.4%
ValueCountFrequency (%)
0 7510
65.7%
1 1279
 
11.2%
2 620
 
5.4%
3 600
 
5.2%
4 248
 
2.2%
5 236
 
2.1%
6 157
 
1.4%
7 119
 
1.0%
8 82
 
0.7%
9 71
 
0.6%
ValueCountFrequency (%)
29 4
 
< 0.1%
28 4
 
< 0.1%
27 19
0.2%
26 7
 
0.1%
25 13
0.1%
24 15
0.1%
23 12
0.1%
22 14
0.1%
21 25
0.2%
20 14
0.1%

Entropy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0.6666666666666666
9190 
0.8333333333333334
1598 
0.5
 
637
1.0
 
5

Length

Max length18
Median length18
Mean length17.15748
Min length3

Characters and Unicode

Total characters196110
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6666666666666666
2nd row0.6666666666666666
3rd row0.8333333333333334
4th row0.5
5th row0.6666666666666666

Common Values

ValueCountFrequency (%)
0.6666666666666666 9190
80.4%
0.8333333333333334 1598
 
14.0%
0.5 637
 
5.6%
1.0 5
 
< 0.1%

Length

2024-02-09T18:26:53.671735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-09T18:26:53.712091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.6666666666666666 9190
80.4%
0.8333333333333334 1598
 
14.0%
0.5 637
 
5.6%
1.0 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
6 147040
75.0%
3 22372
 
11.4%
0 11430
 
5.8%
. 11430
 
5.8%
8 1598
 
0.8%
4 1598
 
0.8%
5 637
 
0.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184680
94.2%
Other Punctuation 11430
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 147040
79.6%
3 22372
 
12.1%
0 11430
 
6.2%
8 1598
 
0.9%
4 1598
 
0.9%
5 637
 
0.3%
1 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 11430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 147040
75.0%
3 22372
 
11.4%
0 11430
 
5.8%
. 11430
 
5.8%
8 1598
 
0.8%
4 1598
 
0.8%
5 637
 
0.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 147040
75.0%
3 22372
 
11.4%
0 11430
 
5.8%
. 11430
 
5.8%
8 1598
 
0.8%
4 1598
 
0.8%
5 637
 
0.3%
1 5
 
< 0.1%

Has_Url_Pattern
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11430
100.0%

Length

2024-02-09T18:26:53.753362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-09T18:26:53.786510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11430
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11430
100.0%

Interactions

2024-02-09T18:26:50.717480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.242265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.679750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.988044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.313636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.632234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.955882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.387762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.756733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.306939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.723159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.029724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.356764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.673549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.007328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.430782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.794703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.393328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.757634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.069585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.397771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.710891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.050292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.468635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.835904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.459406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.798374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.111800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.440051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.753649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.186097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.510508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.874811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.518604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.835048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.150622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.477911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.792733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.223470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.558218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.924821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.558730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.874989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.192544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.518266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.834423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.268066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.600354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.963361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.597739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.911019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.232300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.556031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.872809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.309961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.639109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:51.003548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.635862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:48.949951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.272060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.594146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:49.913609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.348580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T18:26:50.677096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-09T18:26:53.813360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Domain_LengthEntropyHas_File_PathHas_Hyphens_DomainHas_Uncommon_CharsHierarchy_LevelNumber_CountParameter_CountSlash_CountSpecial_Char_CountSubdomain_CountVowel_Consonant_Ratiostatus_numerico
Domain_Length1.0000.1720.0640.4440.150-0.0930.1210.154-0.097-0.0330.4830.2390.276
Entropy0.1721.0000.9980.1290.4760.3400.4130.5610.3640.4830.0920.1120.295
Has_File_Path0.0640.9981.0000.0150.1740.4130.1210.0630.4120.1690.0240.0500.023
Has_Hyphens_Domain0.4440.1290.0151.0000.017-0.0340.1250.156-0.0360.0180.1770.0990.211
Has_Uncommon_Chars0.1500.4760.1740.0171.0000.3480.3680.3350.3590.9730.0150.1930.192
Hierarchy_Level-0.0930.3400.413-0.0340.3481.0000.3360.1510.9900.346-0.0920.2940.232
Number_Count0.1210.4130.1210.1250.3680.3361.0000.3960.3480.3940.1610.0820.096
Parameter_Count0.1540.5610.0630.1560.3350.1510.3961.0000.1710.3460.1810.1510.213
Slash_Count-0.0970.3640.412-0.0360.3590.9900.3480.1711.0000.359-0.0910.2920.230
Special_Char_Count-0.0330.4830.1690.0180.9730.3460.3940.3460.3591.0000.0120.1640.198
Subdomain_Count0.4830.0920.0240.1770.015-0.0920.1610.181-0.0910.0121.0000.0200.268
Vowel_Consonant_Ratio0.2390.1120.0500.0990.1930.2940.0820.1510.2920.1640.0201.0000.113
status_numerico0.2760.2950.0230.2110.1920.2320.0960.2130.2300.1980.2680.1131.000

Missing values

2024-02-09T18:26:51.065965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-09T18:26:51.167097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

urlstatus_numericoSubdomain_CountDomain_LengthHas_File_PathParameter_CountSlash_CountDot_PositionsNumber_CountHierarchy_LevelDomain_ExtensionHas_Uncommon_CharsHas_Hyphens_DomainVowel_Consonant_RatioSpecial_Char_CountEntropyHas_Url_Pattern
0http://www.crestonwood.com/router.php0219113[10, 22, 33]02comFalseFalse0.36363600.6666671
1http://shadetreetechnology.com/V4/validation/a111aedc8ae390eabcfa130e041a10a41123115[26]174comTrueFalse0.82758610.6666671
2https://support-appleld.com.secureupdate.duilawyeryork.com/ap/89e6a3b4b063b8d/?cmd=_update&dispatch=89e6a3b4b063b8d1b&locale=_1450135[23, 27, 40, 54]194comTrueTrue0.51724110.8333331
3http://rgipt.ac.in0211012[12, 15]01inFalseFalse0.30000000.5000001
4http://www.iracing.com/tracks/gateway-motorsports-park/0215115[10, 18]04comFalseFalse0.36363600.6666671
5http://appleid.apple.com-app.es/1324113[14, 20, 28]02esFalseTrue0.50000000.6666671
6http://www.mutuo.it0212012[10, 16]01itFalseFalse0.40000000.5000001
7http://www.shadetreetechnology.com/V4/validation/ba4b8bddd7958ecb8772c836c29695311227115[10, 30]214comTrueFalse0.40540510.6666671
8http://vamoaestudiarmedicina.blogspot.com/0234113[28, 37]02comFalseFalse0.63636400.6666671
9https://parade.com/425836/joshwigler/the-amazing-race-host-phil-keoghan-previews-the-season-27-premiere/0110116[14]85comFalseFalse0.59183700.6666671
urlstatus_numericoSubdomain_CountDomain_LengthHas_File_PathParameter_CountSlash_CountDot_PositionsNumber_CountHierarchy_LevelDomain_ExtensionHas_Uncommon_CharsHas_Hyphens_DomainVowel_Consonant_RatioSpecial_Char_CountEntropyHas_Url_Pattern
11420https://adnanboz.wordpress.com/2012/01/06/how-to-set-up-amazon-ec2-windows-gpu-instance-for-nvidia-cuda-development/0222117[16, 26]96comFalseFalse0.54545500.6666671
11421http://www.peoplemakingplaces.com/includes/Support/En/log/signin/customer_center/customer-IDPP00C644/myaccount/signin12261111[10, 29]510comTrueFalse0.47692370.6666671
11422http://sheetdownload.com/0117113[20]02comFalseFalse0.42857100.6666671
11423http://www.dmega.co.kr/dmega/data/qna/sec/page.php?email=ZmFpdGhAc2VtYW50aWMuaW5mbw==1315117[10, 16, 19, 46]46krTrueFalse0.38297980.8333331
11424http://www.answers.com/Q/What_are_the_sizes_of_computer_memory0215114[10, 18]03comTrueFalse0.44117630.6666671
11425http://www.fontspace.com/category/blackletter0217114[10, 20]03comFalseFalse0.35714300.6666671
11426http://www.budgetbots.com/server.php/Server%20update/index.php?email=USER@DOMAIN.com1218115[10, 21, 32, 58, 80]24comTrueFalse0.488889120.8333331
11427https://www.facebook.com/Interactive-Television-Pvt-Ltd-Group-M-100230523435650/photos/?ref=page_internal0216115[11, 20]154comTrueFalse0.52083370.8333331
11428http://www.mypublicdomainpictures.com/0230113[10, 33]02comFalseFalse0.39130400.6666671
11429http://174.139.46.123/ap/signin?openid.pape.max_auth_age=0&amp;openid.return_to=https%3A%2F%2Fwww.amazon.co.jp%2F%3Fref_%3Dnav_em_hd_re_signin&amp;openid.identity=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.assoc_handle=jpflex&amp;openid.mode=checkid_setup&amp;key=a@b.c&amp;openid.claimed_id=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.ns=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0&amp;&amp;ref_=nav_em_hd_clc_signin13141104[10, 14, 17, 38, 43, 69, 97, 104, 107, 153, 181, 188, 203, 236, 267, 298, 311, 341, 348, 363, 396, 418, 425, 440]413123TrueFalse0.53181870.8333331

Duplicate rows

Most frequently occurring

urlstatus_numericoSubdomain_CountDomain_LengthHas_File_PathParameter_CountSlash_CountNumber_CountHierarchy_LevelDomain_ExtensionHas_Uncommon_CharsHas_Hyphens_DomainVowel_Consonant_RatioSpecial_Char_CountEntropyHas_Url_Pattern# duplicates
0http://e710z0ear.du.r.appspot.com/c:/users/user/downlo142611645comFalseFalse0.5200.66666712